A Data Set of Ion Mobility Collision Cross Sections and Liquid Chromatography Retention Times from 71 Pyridylaminated N-Linked Oligosaccharides.
Noriyoshi ManabeShiho OhnoKana MatsumotoTaiji KawaseKenji HiroseKatsuyoshi MasudaYoshiki YamaguchiPublished in: Journal of the American Society for Mass Spectrometry (2022)
Determination of the glycan structure is an essential step in understanding structure-function relationships of glycans and glycoconjugates including biopharmaceuticals. Mass spectrometry, because of its high sensitivity and mass resolution, is an excellent means of analyzing glycan structures. We previously proposed a method for rapid and precise identification of N -glycan structures by ultraperformance liquid chromatography-connected ion mobility mass spectrometry (UPLC/IM-MS). To substantiate this methodology, we here examine 71 pyridylaminated (PA-) N -linked oligosaccharides including isomeric pairs. A data set on collision drift times, retention times, and molecular mass was collected for these PA-oligosaccharides. For standardization of the observables, LC retention times were normalized into glucose units (GU) using pyridylaminated α-1,6-linked glucose oligomers as reference, and drift times in IM-MS were converted into collision cross sections (CCS). To evaluate the CCS value of each PA-oligosaccharide, we introduced a CCS index which is defined as a CCS ratio of a target PA-glycan to the putative standard PA-glucose oligomer of the same m / z . We propose a strategy for practical structural analysis of N -linked glycans based on the database of m / z , CCS index, and normalized retention time (GU).
Keyphrases
- mass spectrometry
- liquid chromatography
- cell surface
- simultaneous determination
- high resolution mass spectrometry
- high resolution
- tandem mass spectrometry
- solid phase extraction
- high performance liquid chromatography
- gas chromatography
- capillary electrophoresis
- liquid chromatography tandem mass spectrometry
- blood glucose
- type diabetes
- electronic health record
- big data
- single molecule
- emergency department
- molecularly imprinted
- machine learning
- multiple sclerosis
- artificial intelligence
- data analysis
- deep learning
- insulin resistance
- weight loss